Usage-based maintenance service system

ABSTRACT

Systems and methods of providing usage-based maintenance services for a plurality of vehicles arranged in a fleet includes receiving usage data. A usage parameter distribution is generated, and a fleet usage model is generated according to the distribution. The fleet usage model expresses a score as a function of the usage parameter. Also, a score distribution is generated, and a reward model is generated according to the score distribution. The reward model expresses a maintenance reward as a function of the score. Additionally, the score for one of the vehicles is determined using the fleet usage model, and the maintenance reward is determined for the vehicle using the reward model.

TECHNICAL FIELD

The present disclosure generally relates to vehicle maintenance servicesand, more particularly, relates to a usage-based maintenance servicesystem.

BACKGROUND

Vehicles include complex components, such as engine systems, thatrequire regular maintenance. For example, a user can cause wear on avehicle engine over time, and maintenance services can address theengine wear and, in some cases, repair or replace the worn part.Accordingly, the maintenance services can keep the vehicle runningefficiently and dependably.

However, maintenance costs can be expensive, and costs can beunpredictable. Also, the way the vehicle is used may correlate to theamount of wear on the engine. In some scenarios, however, maintenancecosts can be the same for the different users. As such, a person thatcauses less wear can pay the same maintenance fees as another thatcauses more wear.

Thus, there is a need for a system and model that more fairly determinesmaintenance pricing. Other desirable features and characteristics of thesystems and methods of the present disclosure will become apparent fromthe subsequent detailed description and the appended claims, taken inconjunction with the accompanying drawings and the preceding background.

BRIEF SUMMARY

In one embodiment, a method of operating a usage-based maintenancesystem for a plurality of vehicles arranged in a fleet is disclosed. Themethod includes receiving, by a processor, detected usage data from thefleet. The usage data includes a usage parameter for individual ones ofthe plurality of vehicles within the fleet over a predetermined timeperiod. The method includes generating, by the processor from the usagedata, a fleet usage distribution of the usage parameter for theplurality of vehicles across the fleet. Moreover, the method includesgenerating, by the processor, a fleet usage model according to the fleetusage distribution. The fleet usage model expresses a score as afunction of the usage parameter. Furthermore, the method includesgenerating, by the processor, a score distribution of the score for theplurality of vehicles across the fleet. Also, the method includesgenerating, by the processor, a reward model according to the scoredistribution, the reward model expressing a maintenance reward as afunction of the score. The method further includes receiving, by theprocessor, the usage parameter of one of the plurality of vehicles.Additionally, the method includes determining, by the processor usingthe fleet usage model, the score for the one of the plurality ofvehicles according to the received usage parameter for the one of theplurality of vehicles. Moreover, the method includes determining, by theprocessor using the reward model, the maintenance reward for the one ofthe plurality of vehicles according to the score determined for the oneof the plurality of vehicles.

In an additional embodiment, a usage-based maintenance system for aplurality of vehicles arranged in a fleet is disclosed. The systemincludes a data storage device and a processor configured to receivedetected usage data from the fleet. The usage data includes a usageparameter for individual ones of the plurality of vehicles within thefleet over a predetermined time period. The processor is configured togenerate a fleet usage distribution of the usage parameter for theplurality of vehicles across the fleet. The processor is also configuredto generate a fleet usage model according to the fleet usagedistribution. The fleet usage model expresses a score as a function ofthe usage parameter. Moreover, the processor is configured to generate ascore distribution of the score for the plurality of vehicles across thefleet. Also, the processor is configured to generate and save on thedata storage device a reward model according to the score distribution.The reward model expresses a maintenance reward as a function of thescore. The processor is configured to receive the usage parameter of oneof the plurality of vehicles. The processor is configured to determine,using the fleet usage model, the score for the one of the plurality ofvehicles according to the received usage parameter for the one of theplurality of vehicles. Furthermore, the processor is configured todetermine, using the reward model, the maintenance reward for the one ofthe plurality of vehicles according to the score determined for the oneof the plurality of vehicles.

In another embodiment, a method of operating a usage-based maintenancesystem for a plurality of aircraft arranged in a fleet is disclosed. Themethod includes receiving, by a processor, detected usage data thatincludes at least two usage parameters for individual ones of theplurality of vehicles within the fleet over a predetermined time period.The at least two usage parameters are chosen from a group consisting ofa flight length parameter, an environmental exposure parameter, and athrottle setting parameter. Also, the method includes generating, by theprocessor from the detected usage data, a first fleet usage distributionof one of the at least two usage parameters. Furthermore, the methodincludes generating, by the processor from the detected usage data, asecond fleet usage distribution of another of the at least two usageparameters. Also, the method includes generating, by the processor, afirst fleet usage model according to the first fleet usage distribution.The first fleet usage model expresses a first score as a function of theone of the at least two usage parameters. The method further includesgenerating, by the processor, a second fleet usage model according tothe second fleet usage distribution. The second fleet usage modelexpresses a second score as a function of the other of the at least twousage parameters. Additionally, the method includes combining, by theprocessor, the first score and the second score into a combined scorefor individual ones of the plurality of aircraft. Moreover, the methodincludes generating a combined score distribution of the combined scorefor the plurality of aircraft across the fleet. Also, the methodincludes generating, by the processor, a reward model according to thecombined score distribution. The reward model expresses a maintenanceservice discount percentage as a function of the combined score. Themethod also includes receiving, by the processor, the at least two usageparameters of one of the plurality of vehicles. Furthermore, the methodincludes determining, by the processor using the first and second fleetusage models, the first score and the second score for the one of theplurality of vehicles according to the at least two usage parametersreceived for the one of the plurality of vehicles. Also, the methodincludes determining, by the processor, the combined score for the oneof the plurality of vehicles according to the determined first andsecond scores for the one of the plurality of vehicles. Moreover, themethod includes determining, by the processor using the reward model,the maintenance service discount percentage for the one of the pluralityof vehicles according to the combined score determined for the one ofthe plurality of vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will hereinafter be described in conjunction withthe following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a schematic diagram of a system according to exampleembodiments of the present disclosure;

FIG. 2 is a flow chart illustrating a method of operating the system ofFIG. 1 according to example embodiments;

FIG. 3 is a schematic illustration of data processing performedaccording to the method of FIG. 2;

FIG. 4A is a usage model that is configured for evaluating usage of avehicle within the system according to example embodiments of thepresent disclosure;

FIG. 4B is a discount model that is configured for determining amaintenance discount according to detected usage of a vehicle within thesystem;

FIG. 5 is a flow chart illustrating a method of operating the system ofFIG. 1 according to example embodiments;

FIG. 6 is a schematic illustration of data processing performedaccording to the method of FIG. 5; and

FIG. 7 is a schematic illustration of a user interface of the systemaccording to example embodiments of the present disclosure.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the present disclosure or the application and usesof the present disclosure. Furthermore, there is no intention to bebound by any theory presented in the preceding background or thefollowing detailed description.

The present disclosure provides a system and method for pricingmaintenance and/or other services for vehicles and/or the engines of thevehicles. Using the system of the present disclosure and its method ofoperations, pricing for maintenance and/or other services may beadjusted according to certain factors. For example, pricing forservicing an engine may be dependent upon how the engine is used over agiven time period. Specifically, in some embodiments, the system maytrack usage characteristics that correlate directly or indirectly toengine wear. For example, the system may track flight length,environmental exposure, throttle settings, and/or other usagecharacteristics for the engines within a fleet over a predetermined timeperiod.

This data may be used to generate one or more fleet usage models. Thefleet usage model may reflect usage across the fleet for the timeperiod. The models may be utilized for evaluating (scoring) the usagebehavior of different members (persons or organizations participating inthe program). Usage that tends to cause less wear on an engine canreceive a different score from usage that tends to cause more wear onthe engine. A variety of scores may be combined to generate a combinedscore for the member.

Additionally, in some embodiments, the system of the present disclosuremay be used to generate one or more reward models. The reward model maybe generated from usage scores accumulated for the members across thefleet. The reward model may be utilized for determining a reward for thedifferent members based upon their usage scores.

Accordingly, as will be discussed, members that tend to cause less wearon their engine may receive larger rewards than those that tend to causemore engine wear. Rewards can be applied to future maintenance costs insome embodiments.

Moreover, according to the present disclosure, the fleet usage modelsand/or the reward models may be configured and re-configured accordingto important factors. The model(s) may be configured in a way thatensures fairness in the way the rewards are distributed across thefleet. The model(s) may be adjusted over time, if needed, to maintainthis fairness. The models may be tailored (tuned) to ensure that thesystem runs efficiently, effectively, and predictably for both thecustomer and system organizer.

Those of skill in the art will appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Some ofthe embodiments and implementations are described above in terms offunctional and/or logical block components (or modules) and variousprocessing steps. However, it should be appreciated that such blockcomponents (or modules) may be realized by any number of hardware,software, and/or firmware components configured to perform the specifiedfunctions. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps will be described generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the combined system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present disclosure. For example, anembodiment of a system or a component may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments described herein are merelyexemplary implementations.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. The word “exemplary” is used exclusively herein to mean“serving as an example, instance, or illustration.” Any embodimentdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other embodiments. Any of the abovedevices are exemplary, non-limiting examples of a computer readablestorage medium.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in an ASIC. The ASIC mayreside in a user terminal. In the alternative, the processor and thestorage medium may reside as discrete components in a user terminal. Anyof the above devices are exemplary, non-limiting examples of a computerreadable storage medium.

As used herein, the term “module” refers to any hardware, software,firmware, electronic control component, processing logic, and/orprocessor device, individually or in any combination, including withoutlimitation: application specific integrated circuit (ASIC), anelectronic circuit, a processor (shared, dedicated, or group) and memorythat executes one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

In this document, relational terms such as first and second, and thelike may be used solely to distinguish one entity or action from anotherentity or action without necessarily requiring or implying any actualsuch relationship or order between such entities or actions. Numericalordinals such as “first,” “second,” “third,” etc. simply denotedifferent singles of a plurality and do not imply any order or sequenceunless specifically defined by the claim language. The sequence of thetext in any of the claims does not imply that process steps must beperformed in a temporal or logical order according to such sequenceunless it is specifically defined by the language of the claim. Theprocess steps may be interchanged in any order without departing fromthe scope of the invention as long as such an interchange does notcontradict the claim language and is not logically nonsensical.

For the sake of brevity, conventional techniques related to graphics andimage processing, navigation, flight planning, aircraft controls,aircraft data communication systems, and other functional aspects ofcertain systems and subsystems (and the individual operating componentsthereof) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent exemplary functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the subject matter.

In addition, those skilled in the art will appreciate that embodimentsof the present disclosure may be practiced in conjunction with anymethod and/or system associated with gathering engine usage data,generating evaluation models based on the gathered data, and determininguser rewards based on the models. It will also be appreciated that themethods and systems described herein are merely exemplary and configuredaccording to the present disclosure. Further, it should be noted thatmany alternative or additional functional relationships or physicalconnections may be present in an embodiment of the present disclosure.In addition, while the figures shown herein depict examples with certainarrangements of elements, additional intervening elements, devices,features, or components may be present in an actual embodiment.

FIG. 1 depicts an exemplary embodiment of an engine maintenance system100 according to example embodiments of the present disclosure. It willbe understood that FIG. 1 is a simplified representation of the system100 for purposes of explanation and ease of description, and that FIG. 1is not intended to limit the application or scope of the subject matterin any way. Practical embodiments of the system 100 may vary from theillustrated embodiment without departing from the scope of the presentdisclosure. Also, the system 100 may include numerous other devices andcomponents for providing additional functions and features, as will beappreciated in the art.

Generally, the system 100 may include a plurality of vehicles 102 thatare arranged into one or more fleets 101 a, 101 b. In some embodiments,the vehicles 102 may be aircraft; however, it will be appreciated thatthe vehicles 102 may be of another type without departing from the scopeof the present disclosure. In addition to the one or more engines 103,the vehicles 102 may respectively include a computerized terminal device105.

The system 100 may also include a server device 111. The terminaldevices 105 may be in communication with the server device 111 via asuitable communication network 115.

The engines 103 may be gas turbine engines, such as turbofan enginesthat propel the respective vehicle 102 and/or turboshaft engines thatgenerate electric power for the respective vehicle 102. As will bediscussed, the maintenance system 100 may be configured for facilitatingmaintenance on the engines 103 and/or for managing pricing anddiscounting of such maintenance services.

The fleets 101 a, 101 b of vehicles 102 may be arranged in various ways.For example, one fleet 101 a may contain vehicles 102 of a certain typewhile another fleet 101 b may contain vehicles 102 of a different type.In some embodiments, the first fleet 101 a may include vehicles 102 witha configuration of the engine 103 (or engines) that is common to eachwithin the fleet 101 a. In contrast, the second fleet 101 b may includevehicles 102 with a different configuration of engine 103. Accordingly,the vehicles 102 within the fleet 101 a may include the same enginetype, the same number of engines, etc., and the vehicles 102 within theother fleet 101 b may include a different engine type, number ofengines, etc.

The terminal device 105 may be a computerized device that supportsoperations of the system 100. The terminal device 105 of one of thevehicles 102 is illustrated in detail in FIG. 1, and it will beappreciated that the terminal devices 105 may include similar features.As shown, the terminal device 105 may include, without limitation, auser interface 104, a communication system 108, a sensor system 109, anda control system 113, suitably configured to support operation of thesystem 100 as described in greater detail below. The terminal device 105may be incorporated within a flight control system, an electronic flightbag, a portable electronic device, and/or another device that supportsoperation of the system 100. Although the terminal devices 105 arerepresented as being onboard the vehicles 102 in FIG. 1, it will beappreciated that one or more features of the terminal device 105 may beindependent of the vehicle 102 and/or may be a mobile device that isoperable onboard or offboard the vehicle 102. Furthermore, the terminaldevice 105 may be embodied as a desktop computer, a smart phone, atablet, or the like that communicates within the system 100.

The user interface 104 may include an input device with which a user(e.g., a pilot or other crewmember) may input commands, etc. The inputdevice of the user interface 104 may include a keyboard, microphone,touch sensitive surface, control joystick, pointer device, touchsensitive surface such as a touch sensitive display, or other type. Theuser interface 104 may also include an output device that provides theuser with information about the system 100 as will be discussed. Theoutput device of the user interface 104 may include a visual display, aspeaker, etc. The user interface 104 may include a variety of inputand/or output devices. Furthermore, in some embodiments, the userinterface 104 may be used by the pilot or other crew member to controlthe vehicle 102 (e.g., to change the aircraft's speed, trajectory,etc.). The user interface 104 is coupled to and in communication withthe control system 113 and the processor 114 over a suitablearchitecture that supports the transfer of data, commands, power, etc.therebetween. Additionally, the user interface 104 and the processor 114are cooperatively configured to allow a user to interact with otherelements of the system 100 as will be discussed in more detail below.

Moreover, the communication system 108 may include one or more devicesfor communicating data between the server device 111 and one or more ofthe terminal devices 105. In an exemplary embodiment, the communicationsystem 108 is coupled to the control system 113 and the processor 114with a suitable architecture that supports the transfer of data,commands, power, etc. The communication system 108 may be configured tosupport communications to the vehicle 102, from the vehicle 102, and/orwithin the vehicle 102, as will be appreciated in the art. In thisregard, the communication system 108 may be realized using any radio ornon-radio communication system or another suitable data link system. Inan exemplary embodiment, the communication system 108 is suitablyconfigured to support communications between one vehicle 102 and anotheraircraft or ground location (e.g., air traffic control equipment and/orpersonnel).

The sensor system 109 may include one or more sensors configured todetect certain characteristics (usage characteristics) related to theuse of the vehicle 102 and/or engines 103. For example, the sensorsystem 109 may include a timer device 120 that is configured to detectand measure the passage of time. Furthermore, the sensor system 109 mayinclude one or more environment sensors 124. The environment sensor(s)124 may be configured for detecting environmental conditions that affectthe vehicle 102 and its engines 103. For example, the environmentsensor(s) 124 may comprise a salinity sensor configured to detect therespective airborne salinity in the environment of the vehicle 102.Furthermore, the environment sensor 124 may comprise a thermometerconfigured to detect ambient temperature in the environment of thevehicle 102. The environment sensor 124 may comprise a hygrometerconfigured to detect humidity in the environment of the vehicle 102.Also, the environment sensor 124 may comprise a sensor that detectsairborne dust exposure.

The sensor system 109 may, in some embodiments, include and/or may beassociated with systems that are configured to support flight andassociated operations of the vehicle 102. For example, the sensor system109 may be associated with an avionics system 126 of the vehicle 102.

As shown in FIG. 1, the avionics system 112 may include and/or may beassociated with a flight management system (FMS) 130. The FMS 130 may beoperable for obtaining and/or providing real-time flight-relatedinformation. Furthermore, in some embodiments, the FMS 130 maintainsinformation pertaining to a current flight plan (or alternatively, acurrent route or travel plan). Accordingly, the FMS 130 may include oneor more FMS sensors 132 that detect real-time information. Specifically,the FMS sensors 132 may include an altimeter that detects the currentaltitude of the vehicle 102. Also, the FMS sensors 132 may be configuredto detect the current, real-time trajectory of the vehicle 102, theairspeed of the vehicle 102, etc. Additionally, the FMS sensors 132 maydetect the position of the throttle for the vehicle 102.

Moreover, information from the FMS sensors 132 or other system may beused to detect, track, or otherwise identify the current operating state(e.g., flight phase or phase of flight) of the vehicle 102. Variousphases of flight are well known (e.g., a standing phase, a pushback ortowing phase, a taxiing phase, a takeoff phase, a climbing phase, acruising phase, a descent phase, an approach phase, a landing phase, andthe like) and will not be described in detail herein. Also, theoperating state (e.g., flight phase) may be determined according to anengine control system (e.g., a FADEC). Additionally, the flightmanagement system 130 and/or other system may detect the current flightphase indirectly. For example, the FMS sensors 132 may comprise aweight-on-wheels sensor configured to detect that the vehicle 102 islanded. In addition to delineated flight phases, the flight managementsystem 130 may identify other operating states of the vehicle 102 usingthe sensors 132, such as, for example, operation with one or moreengines disabled, operation when afterburners onboard the vehicle 102are being utilized, transonic and/or supersonic operation of the vehicle102, and the like.

Additionally, the avionics system 126 may include or may be associatedwith a navigation system 136 of the vehicle 102 for supportingnavigation operations of the vehicle 102. The navigation system 136 maybe configured to obtain one or more navigational characteristicsassociated with operation of the vehicle 102. Accordingly, thenavigation system 136 may include a positioning sensor 122 that isconfigured to detect a position of the respective vehicle 102. In someembodiments, the positioning sensor 122 may comprise a globalpositioning sensor (GPS) for detecting the global position of therespective vehicle 102; however, it will be appreciated that thepositioning sensor 122 may be of another type without departing from thescope of the present disclosure. As such, the navigation system 128 maybe realized as a global positioning system (GPS), inertial referencesystem (IRS), or a radio-based navigation system (e.g., VHFomni-directional radio range (VOR) or long range aid to navigation(LORAN)), and may include one or more navigational radios or othersensors 122 suitably configured to support operation of the navigationsystem 136, as will be appreciated in the art.

It will be appreciated that the avionics system 126 may include othersub-systems as well without departing from the scope of the presentdisclosure. For example, the avionics system 126 may include a flightcontrol system, an air traffic management system, a radar system, atraffic avoidance system, an enhanced ground proximity warning system,an autopilot system, an autothrust system, a flight control system, aweather system, an electronic flight bag and/or another suitableavionics system.

The control system 113 may be a computerized device that includes atleast one processor 114 and at least one data storage element 116. Thedata storage element 116 may be realized as RAM memory, flash memory,EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. In thisregard, the data storage element 116 can be coupled to the controlsystem 113 and the processor 114 such that the processor 114 can readinformation from (and, in some cases, write information to) the datastorage element 116. In the alternative, the data storage element 116may be integral to the processor 114. As an example, the processor 114and the data storage element 116 may reside in an ASIC. In practice, afunctional or logical module/component of the control system 113 mightbe realized using program code that is maintained in the data storageelement 116.

The processor 114 may include hardware, software, and/or firmwarecomponents configured to facilitate communications and/or interactionsbetween the user interface 104, the communication system 108, the sensorsystem 109, the avionics system(s) 126, and the data storage element116. The processor 114 may also perform additional tasks and/orfunctions described in greater detail below.

Depending on the embodiment, the processor 114 may be implemented orrealized with a general-purpose processor, a content addressable memory,a digital signal processor, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, processing core, discrete hardwarecomponents, or any combination thereof, designed to perform thefunctions described herein. The processor 114 may also be implemented asa combination of computing devices, e.g., a plurality of processingcores, a combination of a digital signal processor and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a digital signal processor core, or any other suchconfiguration. In practice, the processor 114 includes processing logicthat may be configured to carry out the functions, techniques, andprocessing tasks associated with the operation of the system 100, asdescribed in greater detail below. Furthermore, the steps of a method oralgorithm described in connection with the embodiments disclosed hereinmay be embodied directly in hardware, in firmware, in a software moduleexecuted by the processor 114, or in any practical combination thereof.

In some embodiments, the features and/or functionality of the processor114 may be implemented as part of the sensor system 109 for detectingusage characteristics of the respective vehicle 102 and for supportingoperations of the system 100 as will be discussed. Furthermore, theprocessor 114 may be implemented as part of the flight management system130 for managing flight operations. Likewise, the processor 114 may becoupled to the navigation system 136 for obtaining real-timenavigational data and/or information regarding operation of the vehicle102. The processor 114 may also be coupled to the sensor system 109,which in turn, may also be coupled to the FMS 130, the navigation system136, the communication system 108, and one or more additional avionicssystems 126 to support navigation, flight planning, and other aircraftcontrol functions, as well as to provide real-time data and/orinformation regarding operation of the vehicle 102 to the processor 114.Accordingly, as will be discussed, the sensor system 109 of the terminaldevice 105 may detect (i.e., measure) and track usage characteristicsabout the respective vehicle 102 and/or its engine(s) 103 over apredetermined time period. In some embodiments, the sensor system 109may detect a plurality of usage characteristics including, but notlimited to, flight time for the vehicle 102, time spent at differentflight stages, location of the vehicle 102 and/or environmentalconditions at those locations, and/or throttle positions over the timeperiod. This data may be stored at the data storage element 116 in someembodiments. These detected usage characteristics can be utilized,therefore, to characterize how the vehicle 102 and the respectiveengine(s) 103 was used during the given time period. Similarly, theterminal devices 105 of the other vehicles 102 may similarly track theusage characteristics across the fleets 101 a, 101 b.

The usage characteristics detected and tracked by the terminal device105 may be sent (via the communications system 108) to the server device111 for further processing and data analysis. In additional embodiments,the processor 114 may perform local processing and perform at least somedata analysis on the tracked usage characteristics before being sent tothe server device 111 for further processing.

The server device 111 may be a computerized device that generallyincludes one or more processors 140, one or more data storage devices142, and a communication device 143. The server device 111 may enablecentralized computing, at least, with respect to maintenance services,pricing of maintenance services, and/or discounting maintenance servicesfor the engines 103 of the vehicles 102 within the different fleets 101a, 101 b. Accordingly, the server device 111 may be configured as acentral server and a substantial amount of the processing/computing ofvehicle use data, maintenance data, discount data, and/or other data maybe performed by the processor 140 in cooperation with the data storagedevice 134. In some embodiments, the server device 111 may beresponsible for delivering application logic, processing and providingcomputing resources to the terminal devices 105.

The communication device 143 may include one or more devices forcommunicating with the communication systems 108 of the terminal devices105. Usage characteristics (i.e., usage data) tracked and sent by theterminal devices 105 may be communicated to the server device 111 viathe communication device 143.

The processor 140 may include hardware, software, and/or firmwarecomponents configured, for example, to process usage data from theplurality of terminal devices 105. The processor 140 may include variousmodules for performing these tasks based on input received from theterminal devices 105. In some embodiments, the processor 140 may includea distribution module 144 programmed for compiling and generating afleet-wide distributions of the usage data for the engines 103 withinthe system 100. Also, the processor 140 may include a modeler 147. Themodeler 147 may be a module configured to create one or more models fromthe distributions of usage data. The model(s) may be used to evaluateusage of particular engines 103 in comparison with the rest of theengines within the same fleet. The modeler 147 may also generate atleast one model used to determine a maintenance discount according tothese evaluations.

The processor 140 may additionally include a scoring module 148. Thescoring module 148 may utilize the model(s) created by the modeler 147to score (i.e., evaluate) use of an engine 103 in comparison with therest of the usage of engines within the fleet. As will be discussed, theprocessor 140 may receive detected usage characteristics of one of thevehicles 102 within one of the fleets 101 a. Then, the processor 140 maydetermine one or more usage parameters, each indicating a usagecharacteristic for that vehicle 102 (e.g., a flight time usageparameter, an environmental exposure usage parameter, and/or a throttlepower usage parameter). Next, the scoring module 148 may score thedetermined usage parameter according to a respective fleet usage model.The scoring module 148 may rely on a fleet usage model generated by themodeler 147 in order to evaluate a customer's use of an engine 103during a given time period in comparison with usage across the fleet 101a.

Also, the processor 140 may include a discount module 149 (i.e., areward module) programmed to determine a discount or other reward for auser based on the usage score output by the scoring module 148 and basedon the discount model generated by the modeler 147. Furthermore, theprocessor 140 may include a user interface module 146, which isprogrammed to present information about the discount, usage data, andother data to one or more terminal devices 105.

Depending on the embodiment, the processor 140 may be implemented orrealized with a general-purpose processor, a content addressable memory,a digital signal processor, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, processing core, discrete hardwarecomponents, or any combination thereof, designed to perform thefunctions described herein. The processor 140 may also be implemented asa combination of computing devices, e.g., a plurality of processingcores, a combination of a digital signal processor and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a digital signal processor core, or any other suchconfiguration. In practice, the processor 140 includes processing logicthat may be configured to carry out the functions, techniques, andprocessing tasks associated with the operation of the system 100, asdescribed in greater detail below. Furthermore, the steps of a method oralgorithm described in connection with the embodiments disclosed hereinmay be embodied directly in hardware, in firmware, in a software moduleexecuted by the processor 140, or in any practical combination thereof.

The data storage device 142 may be realized as RAM memory, flash memory,EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. In thisregard, the data storage device 142 can be coupled to the processor 140such that the processor 140 can read information from (and, in somecases, write information to) the data storage device 142. In thealternative, the data storage device 142 may be integral to theprocessor 140. As an example, the processor 140 and the data storagedevice 142 may reside in an ASIC. In practice, a functional or logicalmodule/component of the processor 140 might be realized using programcode that is maintained in the data storage device 142. Moreover, thedata storage device 142 may include and/or access databases suitablyconfigured to support operations of the system 100, such as, forexample, a contract database 150, a usage database 152, a map database154, and a model database 156, the contents of which will be discussedin detail below.

The contract database 150 may contain stored contract data for aplurality of individual users (indicated as “user 1” to “user n” in FIG.1). These contracts may be configured in various ways and can includeagreed-to terms for maintenance and maintenance pricing using the system100. In some embodiments, for example, a membership service is providedin which members (“user 1” to “user n”) enroll in a maintenance serviceplan (MSP) that covers maintenance on their vehicle 102 and/or theengine(s) 103 thereon. Members agree to pay an engine hour maintenancefee for future use of an engine 103 for a specified time period. Memberscan pay for engine maintenance services according to a predeterminedper-hour rate. This can be comprehensive coverage that covers repair,replacement, refurbishment, retrofits, modifications, upgrades, usersupport, and the like. Accordingly, the system provides predictabilityregarding maintenance fees for the engines. Thus, members may be betterable to manage future maintenance expenses. The contract database 150may include contract data for each of the members (“user 1” to “usern”). The individual contract terms may differ from each other. Forexample, each contract may include different maintenance rates,different pricing escalation terms, different gratis terms, anddifferent coverage terms, etc. In additional embodiments, the contractsmay include substantially the same terms for each member.

The usage database 152 may store usage data (usage characteristics,usage parameters) that are tracked and received from the terminaldevices 105. Thus, data within the usage database 152 may characterizeusage of the vehicles 102 and/or engines 103 over given time periods.

In some embodiments, the usage data may be organized according toparticular users (“user 1” to “user n”) as indicated in FIG. 1. Theusers may be individual persons, a business organization, or otherentity. However, it will be appreciated that the usage data may beorganized according to the particular vehicle 102, according to theparticular engine 103, or otherwise.

Furthermore, the map database 154 may store maps (map data) of one ormore types. The maps may show environmental conditions for differentmapped regions. In some embodiments, the map database 154 may store oneor more air salinity maps representing the airborne salt content withindifferent territories. In additional embodiments, the map database 154may store weather map data representing ambient temperatures, humidity,air/dust content, or other environmental conditions for differentterritories.

Moreover, the model database 156 may include one or more fleet usagemodels 170 used to evaluate a user's engine usage in comparison withusage within the fleet 101 a, 101 b over the same or similar timeperiods. Using the fleet usage model 170, the processor 140 maydetermine a usage score reflective of this comparison. Also, the modeldatabase 156 may include one or more discounting models 172 used tocalculate a discount for the customer according to their assigned usagescore.

Referring now to FIG. 2, a method 200 of operating the system 100 willbe discussed according to example embodiments. In general, the method200 may be employed for tracking use of the vehicles 102 and the engines103 thereon. Also, the method 200 may be used for collecting this usagedata and performing data analytics for generating one or more of thefleet usage models 170 from the tracked usage data. Additionally, themethod 200 may be used to generate discount models 172 from the trackedusage data. The discount models 172 may be used for determining a user'smaintenance discount for the time period.

As an example, it will be assumed that the method 200 is applied to thefirst fleet 101 a. The method 200 may be similarly applied for vehicles102 and engines 103 of the second fleet 101 b. Also, it will beappreciated that the method 200 may be used for additional fleets ofvehicles and engines.

For the sake of simplicity, it will be assumed that each vehicle 102includes a single engine 103. However, it will be appreciated that themethod 200 may accommodate vehicles 102 with multiple engines 103.

The following discussion will focus on “tracking and detecting usage ofthe engines 103” within the fleet 101 a. It is understood that “trackingand detecting usage of one of the vehicles 102” equates to usage of theengine(s) 103 on that vehicle 102. Thus, these phrases are usedinterchangeably herein. Moreover, the term “usage” is used broadlyherein. In some embodiments, the system 100 may track usagecharacteristics on occasions when the vehicle is in operation (when theengine 103 is powered ON) and on occasions when the vehicle isnonoperative (when the engine 103 is powered OFF).

The method 200 may begin at 202, wherein the terminal devices 105 of thevehicles 102 of the first fleet 101 a track usage data for therespective engines 103. Specifically, the sensor system 109 of onevehicle 102 detects usage characteristics for the engines 103 thereonand provides sensor input to the respective processor 114. In someembodiments, at 202 of the method 200, the sensor system 109 may detectvarious usage conditions, such as flight time, environmental conditions,and/or throttle power settings for the respective engine 103. Theprocessor 114 may save this sensor input in the data storage element116. The terminal devices 105 of the other vehicles 102 may similarlycollect usage data for the other engines 103 within the fleet 101 a.

To detect flight time usage data, the control system 113 may utilize theFMS 130 or other system to distinguish between different flight phases,and the timer device 120 may record time spent between take-off andtouch-down for different flights. In additional embodiments, aweight-on-wheels sensor and the timer device 120 may be used todetermine flight time. This flight time usage data may be stored in thedata storage element 116. In some embodiments, the processor 114 mayprocess this time-of-flight data, for example, to find an average flighttime for the engine 103 over a given time period and/or to determine usecycles for the respective engine 103.

To detect environmental exposure usage data, the sensor system 109 maydetect environmental conditions directly with the environment sensors124. For example, the environment sensor 124 may detect and track theamount of exposure of airborne salinity for the respective engine 103.In other embodiments, the sensor system 109 may utilize the GPS sensorto locate the vehicle 102, and the timer device 120 may time how longthe vehicle 102 spends at the detected location. In some embodiments,the sensor system 109 may locate the vehicle 102 and detect how long thevehicle 102 is parked on ground at the detected location. This locationdata may be stored in the data storage element 116. As will bediscussed, this location data may be correlated with a salinity exposuremap saved in the map database 154 in order to determine the amount ofsalinity exposure.

Furthermore, the sensor system 109 may detect one or more conditionsrelated to throttle power settings (i.e., PLA conditions). For example,the sensor system 109 may measure how the engines 103 are powered duringspecific phases of flight (e.g., at take-off, during climb, and atcruise). In some embodiments, the sensor system 109 may detect how muchtime is spent (over a given time period) with the throttle at a take-offpower level and how much time is spent at a climb power level.Additionally, in some embodiments, the control system 113 may utilizethe FMS 130 or other system to distinguish between different flightphases. The timer device 120 may record time spent at take-off throttlesettings, and this take-off usage data may be stored in the data storageelement 116. Likewise, the timer device 120 may record time spent atclimb throttle settings, and this climb usage data may be stored in thedata storage element 116. Furthermore, the sensor system 109 may detectand track the throttle position when the vehicle 102 is at cruisesettings, and this cruise usage data may be stored in the data storageelement 116.

Next, the method 200 may continue at 204, wherein the usage datarecorded by the plurality of terminal devices 105 is transferred to theserver device 111. At 204 of the method 200, members may upload usagedata to the server device 111 periodically (e.g., once a month).

In other embodiments, the usage data recorded at 202 may beautomatically uploaded to the server device 111. The communicationsystem 108 of the terminal devices 105 may communicate the data to thecommunication device 143 of the server device 111, and the data may besaved at the usage database 152 of the server device 111.

In some embodiments, the processor 140 may further process the usagedata received at 204. This may occur, for example, with regard tosalinity exposure. As mentioned, at 202 of the method 200, the terminaldevice 105 may track the location of the vehicle 102 and how long thevehicle 102 spends parked at the detected location. In this example, at204 of the method 200, the processor 140 of the server device 111 maycorrelate the detected location to a salinity exposure map stored at themap database 154. The map may include a plurality of identified salinityexposure zones having different assigned salinity exposure levels. Anarea near a coastline may have a high salinity exposure level, and anarea further away from the coastline may have a lower salinity exposurelevel. Thus, the processor 140 may determine the amount of salinityexposure according to the detected amount of time spent at the assignedexposure level for the detected location. This information may beexpressed as an “equivalent number of days” spent exposed to airbornesalinity.

Subsequently, the method 200 may continue at 206, wherein the processor140 generates fleet usage models. As shown in FIG. 3, the distributionmodule 144 may receive bulk usage data reported from the terminaldevices 105 of the vehicles 102 within the fleet 101 a. The distributionmodule 144 may be programmed to use statistical analysis to organize theusage data into a plurality of fleet usage distributions.

Specifically, from the usage data received at 204, the distributionmodule 144 may generate a first distribution 220 of flight lengthstatistical data for the first fleet 101 a. The first distribution 220may include the 75th quartile of time (i.e., hours spent in flight) foreach of the engines 103 within the first fleet 101 a. (Average flighttime is plotted on the X-axis, and the number of engines within thefleet 101 a is plotted on the Y-axis.) From the first distribution 220,the distribution module 144 may generate a flight length usage model 221for the fleet 101 a. The flight length usage model 221 may be of avariety of types. For example, the model 221 may be expressed as alinear function (e.g., a piecewise linear function) of the type shown inFIG. 4A. However, it will be appreciated that the model 221 may beexpressed as a nonlinear function in additional embodiments. As will bediscussed, the flight length usage model 221 may be used to evaluate auser's flight length usage characteristics against the rest of the fleet101 a and to assign a corresponding flight length score (S1).

Generally, the flight length usage model 221 may be formulated to, ingeneral, provide larger rewards for users that fly longer flights. Thus,in some embodiments, users that fly longer flights for the monitoredtime period will receive higher scores using the flight length usagemodel 221. Also, as will be discussed, the flight length usage model 221may be tailored (i.e., adapted, adjusted, etc.) to make scoring fairacross the fleet 101 a and/or to achieve other business goals in thereward system 100.

Additionally, from the usage data received at 204, the distributionmodule 144 may generate a second distribution 222 of environmentalexposure statistical data for the first fleet 101 a. (Equivalent timespent in the saline environment is plotted on the X-axis and the numberof engines within the fleet 101 a is plotted on the Y-axis). From thesecond distribution 222, the distribution module 144 may generate anenvironmental exposure usage model 223 for the fleet 101 a. As will bediscussed, the model 223 may be used to evaluate a user's environmentalexposure usage characteristics against the rest of the fleet 101 a andto assign a corresponding exposure score (S2). The model 223 may begenerated to meet various business goals and to establish a fair rewardfor certain members within the fleet 101 a. The model 223 may beformulated to, in general, provide larger rewards for users whoseengines 103 spend less time in salty environments.

Moreover, from the usage data received at 204, the distribution module144 may generate a third distribution 224, a fourth distribution 226,and a fifth distribution 228. The third distribution 224 may includetime spent at takeoff power levels on the X-axis and the correspondingtotal number of engines 103 of the first fleet 101 a on the Y-axis. Thefourth distribution 226 may include time spent at climb power levels onthe X-axis and the corresponding total number of engines 103 of thefirst fleet 101 a on the Y-axis. The fifth distribution 228 may includethe average throttle position (measured in degrees) for the vehicles 102in the first fleet 101 a on the X-axis and the corresponding totalnumber of engines 103 on the Y-axis. From the third distribution 224,the distribution module 144 may generate a take-off usage model 230 forthe fleet 101 a. From the fourth distribution 226, the distributionmodule 144 may generate a climb usage model 232 for the fleet 101 a.From the fifth distribution 228, the distribution module 144 maygenerate a cruise usage model 234 for the fleet 101 a. As will bediscussed, the models 230, 232, 234 may be used to evaluate a user'sthrottle power usage characteristics against the rest of the fleet 101 aand to assign corresponding throttle power scores (S3A, S3B, and S3C,respectively). The models 230, 232, 234 may be generated to meet variousbusiness goals and to establish a fair reward for certain users withinthe fleet 101 a. The models 230, 232, 234 may be formulated to, ingeneral, provide larger rewards for users that fly for less time attake-off power and/or less time at climb power and/or lower throttlesetting at cruise.

In some embodiments, the processor 140 may generate a combined throttlepower model 236 from the distributions 224, 226, 228 and/or from themodels 230, 232, 234. As will be discussed, the combined throttle powermodel 236 may be used to evaluate a user's throttle combined power usagecharacteristics against the rest of the fleet 101 a and to assign acorresponding throttle power score (S3). The model 236 may be generatedto meet various business goals and to establish a fair reward forcertain users within the fleet 101 a. The model 236 may be formulatedto, in general, provide larger rewards for users that fly for less timeat take-off power and/or less time at climb power and/or lower throttlesetting at cruise.

Next, as shown in FIG. 2, the method 200 may continue at 208. At 208,the flight length usage model 221, the environment exposure usage model223, the throttle power usage models 230, 232, 234, and the combinedthrottle power usage model 236 may be saved in the model database 156.

Subsequently, the method 200 may continue at 210. At 210, the processor140 may generate a discount model 240. As represented in FIGS. 3 and 4B,the scoring module 148 may combine the exposure scores S1, S2, S3 andcalculate a combined score for the different engines within the fleet101 a. Thus, the combined score for an engine 103 is expressed as afunction of all three of the scores S1, S2, S3. In some embodiments, thescoring module 148 may weight one of the scores S1, S2, S3 differentlythan another when calculating the combined score. In some embodiments,the scoring module 148 calculates a weighted sum of the scores S1, S2,S3 to produce the combined score. Next, at 210, the distribution module144 may generate a distribution of the combined scores for the firstfleet 101 a. From this distribution, the modeler 147 may generate thediscount model 240 for the fleet 101 a. The discount model 240 may be ofa variety of types. For example, the discount model 240 may be expressedas a linear function (e.g., a piecewise linear function) of the typeshown in FIG. 4B. However, it will be appreciated that the model 240 maybe expressed as a nonlinear function in additional embodiments.

As will be discussed, the discount model 240 may be used to determine amaintenance discount for users within the fleet 101 a according to theusage history reflected in the user's combined score. The discount model240 may be generated to meet various business goals and to establish afair reward for users within the fleet 101 a. According to the discountmodel 240, usage that tends to cause less wear on an engine 103 canresult in larger discounts for the user and vice versa. Also, as will bediscussed, the discount model 240 may be tailored (i.e., adapted,adjusted, etc.) to make the distribution of rewards fair across thefleet 101 a and/or to achieve other business goals in the reward system100.

Then, as shown in FIG. 2, the method 200 may continue at 212. At 212,the discount model 240 may be saved in the model database 156. Next, themethod 200 may terminate.

Referring now to FIG. 4A, an example usage model 310 is illustrated. Themodel 310 may be representative of one or more of the usage models 221,223, 230, 232, 234, which were described above. A correspondingdistribution 320 is shown overlaid for comparison with the model 310,and the distribution 320 may be representative of one or more of thedistributions 220, 222, 224, 226, 228 discussed above. The modeler 147(FIG. 1) may receive one of these distributions and generate at leastpart of the usage model 310 therefrom.

The usage model 310 may be used to evaluate the detected usage of onevehicle 102 against the usage detected for the rest of the fleet 101 afor a given time period. As shown, the model 310 may express a score 312(e.g., ranging between zero and one) as a function of a usage parameter314 (ranging between X and X+n). In some embodiments, the model 310 is alinear function. Also, in some embodiments, the model 310 is a piecewiselinear function that includes a plurality of straight-line sections. Themodel 310 may include a number of points (i.e., knit points, breakpoints, changepoints, threshold point, knots, etc.) that define thepiecewise linear function of the model 310. In some embodiments, themodel 310 may include a first end point 330 (i.e., a first thresholdpoint, a minimum point, etc.). This point 330 may represent a firstthreshold usage parameter, wherein a given usage parameter 314 at orbelow the first end point 330 results in the minimum score 312 (here, ascore of zero (0)). The model 310 may also include a second end point340 (i.e., a second threshold point, a maximum point, etc.). This point340 may represent a second threshold usage parameter, wherein a givenusage parameter 314 at or above the second end point 340 results in themaximum score 312 (here, a score of one (1)). As shown in FIG. 4A, thescore 312 may range between zero (0) and one (1) for usage parameters314 that are between the first and second end points 330, 340. Stateddifferently, the model 310 may further include one or more intermediatepoints that are disposed between the first and second end points 330,340. For example, the model 310 may include a first intermediate point332 and a second intermediate point 334. A first segment 336 extendsbetween the first end point 330 and the first intermediate point 332. Asecond segment 338 extends between the first intermediate point 332 andthe second intermediate point 334. A third segment 339 extends betweenthe second intermediate point 334 and the second endpoint 340. The firstsegment 336 may have a positive slope that is different from (greaterthan) the second and third segments 338, 339. The second and thirdsegments 338, 339 may have a positive slope that is substantially thesame for both. It will be appreciated that the function included in themodel 310 may vary from the illustrated embodiments, may include more orless points, may include more or less segments, may include differentslopes, may be at least partially nonlinear (curved), etc.

Creation of the usage model 310 (at 206 of the method 200) will now bediscussed according to example embodiments. For purposes of discussion,it will be assumed that the model 310 is representative of the flightlength usage model 221. Accordingly, the score 312 on the Y-axis may bethe flight length score, 51, and the usage parameter 314 on the X-axismay be flight length or flight time (FT) for the vehicles 102. Thedistribution 320 may be representative of the first distribution 220.

The usage model 310 may be created using a process of linear regression.Accordingly, the first endpoint 330 and the second endpoint 340 of themodel 310 may be set and selected according to one or moreconsiderations. In some embodiments, the first endpoint 330 may beselected such that a predetermined percentage of the fleet 101 areceives a score 312 of zero (0); therefore, in the present example,vehicles averaging flight lengths less than the first end point 330receive the score of zero (0). Conversely, the second endpoint 340 maybe set and selected such that a predetermined percentage of the fleet101 a receives a score of one (1); therefore, in the present example,vehicles averaging flight lengths more than the second end point 340receive the score of one (1). In some embodiments, the remainingportions of the model 310 may be defined by connecting the first andsecond endpoints 330, 340 with a straight line having a constant slope.In other embodiments (such as the illustrated embodiment), the firstand/or second intermediate points 332, 334 may be set and selected suchthat the slope of the function changes between the end points 330, 340.For example, the first intermediate point 332 may be selected to makethe segment 336 have a higher slope than the second and third segments338, 339. The points 330, 332, 334, 340 may be selected and adjusted to,for example, ensure that the system 100 rewards its users fairly and inan efficient manner. For example, the first and/or second end points330, 340 may be adjusted to change the percentage of members in thefleet 101 a that receive a maintenance discount. Also, the intermediatepoints 332, 334 may be adjusted to ensure the scores 312 aresubstantially evenly distributed for those vehicles having flight lengthusage parameters 314 between the first and second end points 330, 340.

Furthermore, one or more intermediate points may be subsequentlyadjusted. As represented in FIG. 4A, the score 312 for the secondintermediate point 334 may be increased to point 334′ to thereby adjustthe slope of the second and third segments 338′, 339′. Conversely, thescore 312 for the second intermediate point 334 may be decreased topoint 334″ to thereby decrease the slope of the first and secondsegments 338″, 339″. Thus, the function may be tailored, for example, toensure even distribution of the scores for a predetermined percentage ofthe vehicles 102 within the fleet 101 a.

The environmental exposure usage model 233 may be generated similarly.The endpoints 330, 340 may be set such that a predetermined percentageof the fleet 101 a receives a score 312 of zero (0) and such that apredetermined percentage of the fleet 101 a receives a score 312 of one(1). Also, the environmental exposure usage model 233 may include apiecewise linear function therebetween that is defined by one or moreadjustable intermediate points 332, 334. In some embodiments, the slopeof the function in the environmental exposure usage model 233 may beopposite that shown in FIG. 4A. In other words, the environmentalexposure usage model 233 may establish an inverse relationship betweenthe score 312 and the environmental exposure usage parameter 314.Accordingly, higher scores may be awarded to those vehicles 102 that hadless exposure to harsh environments.

Likewise, the throttle position models 230, 232, 234 may be similarlygenerated. A different piecewise linear function may be generated foreach. Endpoints 330, 340 for each function may be set such that apredetermined percentage of the fleet 101 a receives a score 312 of zero(0) and such that a predetermined percentage of the fleet 101 a receivesa score 312 of one (1). Also, the slope of the segments in the functionmay be set and/or adjusted according to the intermediate point(s)therebetween. Like the environmental exposure usage model 233, thefunctions for the throttle position models 230, 232, 234 may havenegative slopes. Accordingly, higher scores may be awarded to thosevehicles 102 that put less strain on the engine due to the throttleposition.

Accordingly, the models 221, 223, 230, 232, 234 may be tailoredaccording to the usage data. The models 221, 223, 230, 232, 234 may betailored to ensure that scores 312 are distributed evenly for apredetermined percentage of the fleet 101 a. Also, it will beappreciated that the model(s) may be adjusted over time, if needed, tomaintain this fairness. The models may be tailored (tuned) to ensurethat the system runs efficiently, effectively, and predictably for boththe customer and system organizer.

Referring now to FIG. 4B, an example discount model 350 is illustrated.The model 350 may be representative of the discount model 240, which wasdescribed above. The discount model 350 may be used to determine adiscount for particular members based on their combined usage score (acombination of the scores S1, S2, and S3 as described above). As shown,the model 350 may express a discount rate 352 (ranging between 0% and Y%) as a function of the combined score 354 (ranging between zero (0) andone (1)). In some embodiments, the model 350 is a linear function. Also,in some embodiments, the model 350 is a piecewise linear function thatincludes a plurality of straight-line sections. The model 350 mayinclude a number of points (i.e., knit points, break points,changepoints, threshold values, knots, etc.) that define the piecewiselinear function of the model 350. It will be appreciated, however, thatthe function may vary from the illustrated embodiment. For example, insome embodiments, the function may be at least partially nonlinear(curved), etc.

In some embodiments, the model 350 may include a first end point 360(i.e., a first threshold point, a minimum point, etc.). This point 360may represent a first threshold score, wherein a given score 354 at orbelow the first end point 360 results in the minimum discount 352 (here,a discount of zero percent (0%)). The model 360 may also include asecond end point 370 (i.e., a second threshold point, a maximum point,etc.). This point 370 may represent a second threshold score, wherein agiven score at or above the second end point 370 results in the maximumdiscount 352 (here, a discount of Y percent (Y %)). As shown in FIG. 4B,the discount rate may range between zero percent (0%) and Y percent (Y%) for scores 354 that are between the first and second end points 360,370. Stated differently, the model 350 may further include one or moreintermediate points that are disposed between the first and second endpoints 360, 370. For example, the model 350 may include an intermediatepoint 362 disposed between the first and second end points 360, 370. Afirst segment 364 extends between the first end point 360 and theintermediate point 362. A second segment 366 extends between theintermediate point 362 and the second end point 370. The first segment364 may have a positive slope that is different from (less than) thesecond segment 366. It will be appreciated that the function included inthe model 350 may vary from the illustrated embodiments, may includemore or less points, may include more or less segments, may includedifferent slopes, may be at least partially nonlinear (curved), etc.

Creation of the discount model 350 (at 210 of the method 200) will nowbe discussed according to example embodiments. The discount model 350may be created using a process of linear regression. Accordingly, thefirst endpoint 360 and the second endpoint 370 of the model 350 may beset and selected according to one or more considerations. In someembodiments, the first endpoint 360 may be selected such that apredetermined percentage of the fleet 101 a receives no discount (adiscount rate of 0%). Conversely, the second endpoint 370 may be set andselected such that a predetermined percentage of the fleet 101 areceives a discount of Y %. In some embodiments, the remaining portionsof the model 350 may be defined by connecting the first and secondendpoints 360, 370 with a straight line having a constant slope. Inother embodiments (such as the illustrated embodiment), the intermediatepoint 362 may be set and selected such that the slope of the functionchanges between the end points 360, 370. For example, the intermediatepoint 362 may be selected to change the slopes of the segments 364, 366.The points 360, 362, 370 may be selected and adjusted to, for example,ensure that the system 100 rewards its users fairly and in an efficientmanner. For example, the intermediate point 362 may be adjusted toensure the discounts are substantially evenly distributed for thosevehicles having combined scores that are between the first and secondend points 360, 370. Furthermore, one or more intermediate points may besubsequently adjusted. As represented in FIG. 4B, the discount rate 352for the intermediate point 362 may be increased to point 362′ to therebyadjust the slope of the segments 364′, 366′. Conversely, the discountrate for the intermediate point 362 may be decreased to point 362″ tothereby decrease the slope of the segments 364″, 366″. Thus, thefunction may be tailored, for example, to ensure even distribution ofthe discounts for a predetermined percentage of the vehicles 102 withinthe fleet 101 a.

Referring now to FIG. 5, a method 400 of operating the system 100 willbe discussed according to example embodiments. In general, the method400 may be employed for determining, for a time period, usage parametersof a particular vehicle 102. These usage parameters indicate usagecharacteristics of that vehicle 102 over the time period. The method 400may also be used to score the determined usage parameters according tothe fleet usage models 221, 223, 236 to produce a combined usage score.Additionally, the method 400 may be used to determine a maintenancediscount according to the combined usage score using the discount model240.

The method 400 may begin at 402, wherein usage parameters for therespective engine 103 are determined for a given time period (e.g., onemonth). Continuing with the example discussed in relation to FIGS. 2 and3, at 402 of the method 400, a flight time usage parameter can bedetermined to indicate how long the vehicle 102 spent in-flight duringthe time period. Also, an environmental exposure usage parameter can bedetermined to indicate how much the vehicle 102 was exposed tohigh-salinity environments during the time period. Moreover, a throttlepower usage parameter may be determined to indicate how the engine 103was powered during the time period. Accordingly, 402 of the method 400may substantially correspond (and, in some embodiments coincide) with202 of the method 200. The usage parameters may be saved at the usagedatabase 152 of the server device 111.

Specifically, at 402 of the method 400, the sensor system 109 may detectdifferent flight phases of the vehicle 102 using the FMS sensor 132 orother system, and the timer device 120 may record time spent betweentake-off and touch-down for different flights. In some embodiments, theprocessor 114 or processor 140 may process this time-of-flight data, forexample, to find an average flight time for the engine 103 over the timeperiod and/or to determine use cycles for the respective engine 103.Accordingly, an average flight time parameter 452 (FIG. 5) over the timeperiod may be determined for the vehicle 102.

Also, at 402 of the method 400, the sensor system 109 may locate thevehicle 102 during the time period using the positioning sensor 122. Thetimer device 120 may also detect the amount of time the vehicle 102spends at the detected location(s). In some embodiments, the timerdevice 120 may record how long the vehicle 102 spends parked at thedetected location(s). Also, the processor 140 may correlate the detectedlocation(s) with one or more maps stored at the map database 154. Themap may include a plurality of identified salinity exposure zones, andthe zones may have different assigned exposure levels. The processor 140may determine an environment exposure parameter 454 (FIG. 5) accordingto the detected amount of time spent at the assigned exposure level forthe detected location. In some embodiments, the environment exposureparameter 454 may be expressed as an equivalent number of days spent ina high salinity environment.

Moreover, at 402 of the method 400, the sensor system 109 may detectdifferent flight phases of the vehicle 102 using the FMS sensor 132 orother system, and the timer device 120 may record time spent at take-offthrottle settings. Additionally, the timer device 120 may record timespent at climb throttle settings. Furthermore, the sensor system 109 maydetect and track the throttle position when the vehicle 102 is at cruisesettings. The processor 114 or the processor 140 may process this dataand determine multiple throttle parameters 456 (FIG. 5), including anaverage time spent at take-off throttle settings for the time period,average time spent at climb throttle settings for the time period, andan average throttle position (measured in degrees) at cruise settingsfor the time period.

The method 400 may continue at 404, wherein the scoring module 148generates usage scores according to the usage parameters 452, 454, 456determined at 402. As such, the scoring module 148 evaluates usagehistory of the tracked vehicle 102 and/or engine 103 in comparison withthe rest of the fleet 101 a. Specifically, as represented in the dataflow process 450 of FIG. 6, the scoring module 148 may utilize the fleetflight time model 221 and generate a flight time score 51 according tothe flight time parameter 452 determined at 402. As represented in FIG.4A, if the flight time usage parameter is detected (at 402) at point 399on the X-axis, then the flight time score 51 would equal approximately0.8. The usage score may be saved at the usage database 152 for theparticular user.

Similarly, the scoring module 148 may utilize the fleet exposure model223 and generate an exposure score S2 according to the environmentexposure parameter 454 determined at 402. The exposure score S2 mayrange between zero and one in some embodiments, with higher amounts ofexposure receiving scores closer to zero and vice versa.

Furthermore, the scoring module 148 may utilize the fleet exposuremodels 230, 232, 234 and generate throttle scores 464 according to thethrottle power parameters 456 determined at 402. Using the take-offusage model 230, the processor 140 may generate a take-off score S3Aaccording to the average take-off time parameter determined at 402. Thetake-off score S3A may range between zero and one in some embodiments,with lower average take-off times receiving scores closer to one andvice versa. Moreover, using the climb usage model 232, the processor 140may generate a climb score S3B according to the average climb timeparameter determined at 402. The climb score S3B may range between zeroand one in some embodiments, with lower average climb times receivingscores closer to one and vice versa. Additionally, using the cruiseusage model 234, the processor 140 may generate a cruise score S3Caccording to the average cruise throttle position parameter determinedat 402. The cruise score S3C may range between zero and one in someembodiments, with lower average cruise throttle positions receivingscores closer to one and vice versa. In some embodiments, these threethrottle scores S3A, S3B, S3C may be combined into the single combinedthrottle power score S3 according to the combined throttle power model236. For example, the processor 140 may weight the three throttle scoresS3A, S3B, S3C to produce the combined throttle power score S3. In otherwords:S3=a*S3A+b*S3B+c*S3Cwhere a, b, and c, are the applied weight variables, and where the sumof a, b, and c is equal to one (1). In some embodiments, the processor140 may weight the three throttle scores S3A, S3B, S3C equally (i.e., a,b, and c are equal to ⅓); however, it may be appreciated that moreweight may be applied to one throttle score than another.

The method 400 may continue at 406, wherein the scoring module 148combines the flight time score S1, the exposure score S2, and thethrottle power score S3 and generates a combined usage score 468 for thevehicle 102 and engine(s) 103 tracked at 402. The combined usage score468 may be saved at the usage database 152 of the server device 111.

In some embodiments, represented in FIG. 6, the processor 140 may applydifferent weights 466 to the flight time score S1, the exposure scoreS2, and the throttle score S3 to produce the combined usage score 468.For example, average flight time may have the strongest correlation toengine wear rate. Therefore, the flight time score S1 may be weighedheavier than the exposure score S2 and the throttle score S3. Also, theamount of environment exposure may have the next highest correlation toengine wear rate. Thus, the exposure score S2 may be weighted heavierthan the throttle power score S3. The throttle power parameters may havethe loosest correlation to engine wear; therefore, the processor 140 mayapply the smallest weight to the throttle score S3. Accordingly, in someembodiments, the combined usage score 468 may range between zero andone. Combined usage scores 468 closer to one may reflect usage thattends to cause less wear on the engine 103. Scores closer to zero mayreflect usage that tends to cause more wear on the engine 103.

Next, at 408 of the method 400, the discount module 149 may determine adiscount for the user of the vehicle 102 and engine(s) tracked at 402.The discount module 149 may utilize the discount model 240 to determinea discount 470 according to the combined usage score 468. A highercombined usage score 468 may result in a higher discount 470, and alower combined usage score 468 may result in a smaller discount 470. Asrepresented in FIG. 4B, if the combined score is calculated to be atpoint 499 on the X-axis, then the discount percentage would equalapproximately Y/2. The processor 140 may access the contract database150 and correlate the discount 470 with the contract for thecorresponding user.

Then, at 410 of the method 400, information about the discount 470 maybe communicated to the user. For example, the server device 111 may sendcontrol commands to the terminal device 105 of the vehicle 102 trackedat 402. The control commands may cause the user interface 104 to outputthe calculated discount 470. In some embodiments, the discount 470 maybe displayed visually by the user interface 104.

In some embodiments represented in FIG. 6, the user interface 104 maydisplay a user's contract number along with a visual representation oftheir usage scores for the past month. The fleet average may also bedisplayed for purposes of comparison. The “current month savings” and“current month discount” (calculated at 408) may be displayed as well.Additionally, past usage and/or past discount information from anothertime period may also be displayed.

Accordingly, the system 100 and methods 200, 400 of the presentdisclosure provide fairer pricing for maintenance and/or other services.Users that use the engine in a manner which results in lower maintenancecosts can earn higher discounts than users that put more strain on theirengine. Also, users may be incentivized to use a vehicle 102 and itsengine(s) 103 in a manner that causes less wear over time. Additionally,the models used for adjusting and determining user discounts can beformulated for efficiently and effectively rewarding users at differentlevels based on their usage history. The fleet usage models and/or thediscount models may be configured and re-configured according toimportant factors, achieving fairness, efficient use of resources, andproviding predictability. Furthermore, the system 100 and its methods200, 400 can provide useful information to users about their usagehistory and how it compares to the rest of the fleet.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thepresent disclosure in any way. Rather, the foregoing detaileddescription will provide those skilled in the art with a convenient roadmap for implementing an exemplary embodiment of the present disclosure.It being understood that various changes may be made in the function andarrangement of elements described in an exemplary embodiment withoutdeparting from the scope of the present disclosure as set forth in theappended claims.

What is claimed is:
 1. A method of operating a usage-based maintenancesystem for a plurality of vehicles arranged in a fleet, the methodcomprising: receiving, by a processor, detected usage data from thefleet, the usage data including a usage parameter for individual ones ofthe plurality of vehicles within the fleet over a predetermined timeperiod; generating, by the processor from the usage data, a fleet usagedistribution of the usage parameter for the plurality of vehicles acrossthe fleet; generating, by the processor, a fleet usage model accordingto the fleet usage distribution, the fleet usage model expressing ascore as a function of the usage parameter; generating, by the processora score distribution of the score for the plurality of vehicles acrossthe fleet; generating, by the processor, a reward model according to thescore distribution, the reward model expressing a maintenance reward asa function of the score; receiving, by the processor, the usageparameter of one of the plurality of vehicles; determining, by theprocessor using the fleet usage model, the score for the one of theplurality of vehicles according to the received usage parameter for theone of the plurality of vehicles; and determining, by the processorusing the reward model, the maintenance reward for the one of theplurality of vehicles according to the score determined for the one ofthe plurality of vehicles.
 2. The method of claim 1, wherein the fleetusage model includes a first piecewise linear function expressing thescore as a function of the usage parameter; and wherein the reward modelincludes a second piecewise linear function expressing the maintenancereward as a function of the score.
 3. The method of claim 2, wherein thefirst piecewise linear function defines a first threshold usageparameter and a second threshold usage parameter; wherein, according tothe first piecewise linear function, the score ranges between a minimumscore and a maximum score for usage parameters that are between thefirst and second threshold usage parameters.
 4. The method of claim 3,wherein the first piecewise linear function defines an intermediatepoint corresponding to an intermediate usage parameter and anintermediate score, the intermediate usage parameter being between thefirst and second threshold usage parameters, the intermediate scorebeing between the minimum and maximum scores; further comprisingadjusting the intermediate point to adjust the first piecewise linearfunction.
 5. The method of claim 2, wherein the second piecewise linearfunction defines a first threshold score and a second threshold score;wherein, according to the second piecewise linear function, themaintenance reward ranges between a minimum reward and a maximum rewardfor scores that are between the first and second threshold scores. 6.The method of claim 5, wherein the second piecewise linear functiondefines an intermediate point corresponding to an intermediate score andan intermediate reward, the intermediate score being between the firstand second threshold scores, the intermediate reward being between theminimum and maximum rewards; further comprising adjusting theintermediate point to adjust the second piecewise linear function. 7.The method of claim 1, wherein the usage parameter includes at least oneof: a flight time usage parameter indicating time spent in-flight duringthe time period; an environmental exposure usage parameter indicating anamount of exposure to an environment during the time period; and athrottle power usage parameter indicating powering of an engine of thevehicle during the time period.
 8. The method of claim 7, wherein theusage data includes at least two of the flight time usage parameter, theenvironmental exposure usage parameter, and the throttle power usageparameter; wherein generating the fleet usage distribution includesgenerating a first fleet usage distribution of one of the at least twoof the flight time usage parameter, the environmental exposure usageparameter, and the throttle power usage parameter; further comprisinggenerating a second fleet usage distribution of another of the at leasttwo of the flight time usage parameter, the environmental exposure usageparameter, and the throttle power usage parameter; further comprisinggenerating a first usage model according to the first fleet usagedistribution and a second usage model according to the second fleetusage distribution, the first fleet usage model expressing a first scoreas a function of the one of the at least two of the flight time usageparameter, the environmental exposure usage parameter, and the throttlepower usage parameter, the second fleet usage model expressing a secondscore as a function the other of the at least two of the flight timeusage parameter, the environmental exposure usage parameter, and thethrottle power usage parameter; and wherein generating the scoredistribution includes combining the first score and the second scoreinto a combined score for individual ones of the plurality of vehiclesand generating the score distribution of the combined score for thefleet.
 9. The method of claim 8, wherein combining the first score andthe second score includes weighting the first score and the second scoredifferently to produce a combined weighted usage score; and whereindetermining the maintenance discount includes determining themaintenance discount according to the combined weighted usage score. 10.The method of claim 1, wherein the maintenance reward is a discountpercentage on maintenance pricing.
 11. The method of claim 1, furthercomprising displaying the maintenance reward determined for the one ofthe plurality of vehicles.
 12. A usage-based maintenance system for aplurality of vehicles arranged in a fleet, the system comprising: a datastorage device; and a processor configured to receive detected usagedata from the fleet, the usage data including a usage parameter forindividual ones of the plurality of vehicles within the fleet over apredetermined time period; the processor configured to generate a fleetusage distribution of the usage parameter for the plurality of vehiclesacross the fleet; the processor configured to generate a fleet usagemodel according to the fleet usage distribution, the fleet usage modelexpressing a score as a function of the usage parameter; the processorconfigured to generate a score distribution of the score for theplurality of vehicles across the fleet; the processor configured togenerate and save on the data storage device a reward model according tothe score distribution, the reward model expressing a maintenance rewardas a function of the score; the processor configured to receive theusage parameter of one of the plurality of vehicles; the processorconfigured to determine, using the fleet usage model, the score for theone of the plurality of vehicles according to the received usageparameter for the one of the plurality of vehicles; and the processorconfigured to determine, using the reward model, the maintenance rewardfor the one of the plurality of vehicles according to the scoredetermined for the one of the plurality of vehicles.
 13. The system ofclaim 12, wherein the fleet usage model includes a first piecewiselinear function expressing the score as a function of the usageparameter; and wherein the reward model includes a second piecewiselinear function expressing the maintenance reward as a function of thescore.
 14. The system of claim 13, wherein the first piecewise linearfunction defines a first threshold usage parameter and a secondthreshold usage parameter; wherein, according to the first piecewiselinear function, the score ranges between a minimum score and a maximumscore for usage parameters that are between the first and secondthreshold usage parameters.
 15. The system of claim 14, wherein thefirst piecewise linear function defines an intermediate pointcorresponding to an intermediate usage parameter and an intermediatescore, the intermediate usage parameter being between the first andsecond threshold usage parameters, the intermediate score being betweenthe minimum and maximum scores; wherein the intermediate point isadjustable for adjusting the first piecewise linear function.
 16. Thesystem of claim 13, wherein the second piecewise linear function definesa first threshold score and a second threshold score; wherein, accordingto the second piecewise linear function, the maintenance reward rangesbetween a minimum reward and a maximum reward for scores that arebetween the first and second threshold scores.
 17. The system of claim16, wherein the second piecewise linear function defines an intermediatepoint corresponding to an intermediate score and an intermediate reward,the intermediate score being between the first and second thresholdscores, the intermediate reward being between the minimum and maximumrewards; further comprising adjusting the intermediate point to adjustthe second piecewise linear function.
 18. The system of claim 12,further comprising a sensor system configured to detect the usageparameter from at least one of: a flight time usage parameter indicatingtime spent in-flight during the time period; an environmental exposureusage parameter indicating an amount of exposure to an environmentduring the time period; and a throttle power usage parameter indicatingpowering of an engine of the vehicle during the time period.
 19. Thesystem of claim 18, wherein the sensor system is configured to detect atleast two of the flight time usage parameter, the environmental exposureusage parameter, and the throttle power usage parameter; wherein theprocessor is configured to generate the fleet usage distribution toinclude a first fleet usage distribution of one of the at least two ofthe flight time usage parameter, the environmental exposure usageparameter, and the throttle power usage parameter; wherein the processoris configured to generate a second fleet usage distribution of anotherof the at least two of the flight time usage parameter, theenvironmental exposure usage parameter, and the throttle power usageparameter; wherein the processor is configured to generate a first usagemodel according to the first fleet usage distribution and a second usagemodel according to the second fleet usage distribution, the first fleetusage model expressing a first score as a function of the one of the atleast two of the flight time usage parameter, the environmental exposureusage parameter, and the throttle power usage parameter, the secondfleet usage model expressing a second score as a function the other ofthe at least two of the flight time usage parameter, the environmentalexposure usage parameter, and the throttle power usage parameter; andwherein the processor is configured to generate the score distributionby combining the first score and the second score into a combined scorefor individual ones of the plurality of vehicles and generate the scoredistribution of the combined scores for the fleet.
 20. A method ofoperating a usage-based maintenance system for a plurality of aircraftarranged in a fleet, the method comprising: receiving, by a processor,detected usage data that includes at least two usage parameters forindividual ones of the plurality of vehicles within the fleet over apredetermined time period, the at least two usage parameters chosen froma group consisting of a flight length parameter, an environmentalexposure parameter, and a throttle setting parameter; generating, by theprocessor from the detected usage data, a first fleet usage distributionof one of the at least two usage parameters; generating, by theprocessor from the detected usage data, a second fleet usagedistribution of another of the at least two usage parameters;generating, by the processor, a first fleet usage model according to thefirst fleet usage distribution, the first fleet usage model expressing afirst score as a function of the one of the at least two usageparameters; generating, by the processor, a second fleet usage modelaccording to the second fleet usage distribution, the second fleet usagemodel expressing a second score as a function of the other of the atleast two usage parameters; combining, by the processor, the first scoreand the second score into a combined score for individual ones of theplurality of aircraft; generating a combined score distribution of thecombined score for the plurality of aircraft across the fleet;generating, by the processor, a reward model according to the combinedscore distribution, the reward model expressing a maintenance servicediscount percentage as a function of the combined score; receiving, bythe processor, the at least two usage parameters of one of the pluralityof vehicles; determining, by the processor using the first and secondfleet usage models, the first score and the second score for the one ofthe plurality of vehicles according to the at least two usage parametersreceived for the one of the plurality of vehicles; determining, by theprocessor, the combined score for the one of the plurality of vehiclesaccording to the determined first and second scores for the one of theplurality of vehicles; and determining, by the processor using thereward model, the maintenance service discount percentage for the one ofthe plurality of vehicles according to the combined score determined forthe one of the plurality of vehicles.